The problem
Knowledge existed, but teams could not reliably find or reuse it.
The client managed research across distributed teams producing analyses, literature reviews, and findings stored in large PDF repositories. Prior work existed across the corpus but was difficult to discover. Findings stayed siloed, and extracting a specific point from a dense document often meant reading the file end to end.
The result was repeated analysis and budget spent rediscovering conclusions that had already been reached.
The missing layer
The client needed a single system that could parse complex files, centralize the corpus, search by meaning rather than exact keywords, and expose reliable evidence through a conversational interface.
The solution
A custom pipeline from document parsing to conversational retrieval.
Accurate document parsing
The workflow starts with Unstructured, because downstream retrieval quality depends on reliably extracting content from complex PDFs.
Semantic retrieval
Qdrant indexes content by meaning, allowing researchers to locate relevant documents even when their query wording differs from the source text.
Contextual querying and cross-referencing
A conversational interface powered by Gemini lets researchers query the indexed corpus and surface connections across documents in a single interaction.
Deployment architecture
Built to scale with documents and teams.
The platform uses a microservices-based MCP (Model Context Protocol) architecture, containerized with Docker and deployed on GCP Cloud Run with FastAPI services.
The outcome
Research began functioning as shared infrastructure.
Teams could discover prior work, cross-reference findings, and reuse organizational knowledge.
The supplied case study reports reduced redundant research effort, faster research workflows, and improved cross-team access to shared knowledge. Work produced by one team became visible and queryable by others instead of remaining isolated in files.